1. Technical Field
The present disclosure relates to object detection within images, and more particularly to a variational level set system and method for shape-driven knowledge-based object detection.
2. Discussion of Related Art
Recovering a particular structure of interest from an image that follows some predefined characteristics is useful for model-based image segmentation. Such characteristics may be visual or geometric. Visual properties may be captured by building global distributions to describe the luminance characteristics of the structure of interest or by creating local appearance models. Such models can be efficient in a constrained illumination scenario, where changes are minimal and can be captured by a model.
Shape-driven knowledge-based segmentation is an alternative to visual-driven techniques. Such methods aim to recover a structure that has consistent geometric form when compared to a prior model. Smoothness is an example of imposing a prior constraint. Local geometric properties (e.g., curvature, local smoothness constraints) can be used when defining such a prior model or a prior model may be defined in a more global manner leading to more concrete representations that capture the variance of the entire structure of interest. While local models are efficient, global representations are an appropriate approach to cope with occlusions, noise and changes on the object pose.
Modeling is needed prior of introducing global shape-driven constraints. Such task is equivalent with extracting a compact representation for the structure of interest from a set of training examples. The selection of representation is related to the form of prior that is to be introduced and is constrained by the size of the training set. Building complex models requires significant amount of ground truth. Registration of all examples to a common pose is an important part of the modeling phase. Correspondences for the basic elements of the samples of the training are to be recovered towards efficient modeling.
Shape-driven knowledge-based segmentation involves a wide variety of models. Use of geometric components, like straight segments and ellipsoids was an attempt to create a compact representation for modeling faces. While such models are efficient in terms of performance and low complexity when modeling simple geometric structures, they fail to account for local information and important variability of the object of interest. Given such model, segmentation is then performed through the adjustment of the local geometric components towards the desired image properties.
Other techniques includes deformable templates, active shape and appearance models, and snake models.
The application domain for level set methods in machine vision is wide and not restricted to image segmentation, restoration, tracking, shape from shading, 3D reconstruction, medical image segmentation, etc. These techniques were originated, studied and applied to other scientific domains like geometry, robotics, fluids, semiconductors designing, etc. Most of the mentioned applications share a common concern, tracking moving interfaces. Level set representations are well-suited computational methods to perform this task. They can be used for any dimension (e.g., curves, surfaces, hyper-surfaces, etc.), are parameter free and can change naturally the topology of the evolving interface. Moreover, they provide a natural way to determine and estimate geometric properties of the evolving interface.
These techniques can also deal with non-rigid objects and motions, since they refer to very local characteristics and can deform an interface pixel-wise. However, they can exhibit poor performance compared with parametric models when solid/rigid motions and objects are considered. Local propagations are sensitive and fail to take fully advantage of some a priori well-determined physical constraints like solid shape models.
Therefore, a need exists for a variational level set system and method for shape-driven knowledge-based object detection.